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add ablations
This commit is contained in:
Yoyo
parent
6d0afeacd3
commit
841849cdb8
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@ -25,4 +25,15 @@ uv run maturin develop --release --manifest-path rustbpe/Cargo.toml
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python -m nanochat.dataset -n 240
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# modal set up
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pip install modal
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pip install modal
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uv run modal setup
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uv run modal secret create nanochat-secrets \
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WANDB_API_KEY=<your_wandb_key> \
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HF_TOKEN=hf_<your_hf_token>
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# running models - part 2 ablation studies
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- first time: uv run modal run nanochat_modal.py::main
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- re-run one ablation after data/tokenizer are already on the volume
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uv run modal run nanochat_modal.py::run_baseline
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uv run modal run nanochat_modal.py::run_swiglu
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uv run modal run nanochat_modal.py::run_rope500k
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345
nanochat_modal.py
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345
nanochat_modal.py
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@ -0,0 +1,345 @@
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"""
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Ablation study: picochat (depth=8) — baseline vs SwiGLU vs RoPE-500K
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Setup (one-time):
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modal setup # authenticate (yoyoliuuu workspace)
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modal secret create nanochat-secrets \\
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WANDB_API_KEY=<your_key> \\
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HF_TOKEN=hf_<your_token>
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Run all 3 ablations end-to-end (data + tokenizer once, then 3 training runs):
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modal run nanochat_modal.py
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Run individual stages:
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modal run nanochat_modal.py::stage_data
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modal run nanochat_modal.py::stage_tokenizer
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modal run nanochat_modal.py::run_baseline
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modal run nanochat_modal.py::run_swiglu
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modal run nanochat_modal.py::run_rope500k
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Cost reference (A10G at ~$1.10/hr):
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picochat d8, 1 GPU: ~1-2 hr per run → ~$1.50-3 per run
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3 runs total: ~$5-9
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Notes:
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- Data and tokenizer are cached in a persistent Modal Volume.
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All 3 runs share the same data/tokenizer — download only happens once.
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- Stages are idempotent where possible.
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- The nanochat repo is copied into the container image at build time.
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If you change gpt.py or base_train.py, Modal auto-rebuilds the image.
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- W&B runs go to the yoyoliuuu workspace under project "nanochat".
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Each run is tagged: picochat-baseline, picochat-swiglu, picochat-rope500k.
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Reference: Angela Sha, https://github.com/UofT-CSC490-W2026/022326-tutorial-nanochat
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"""
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import os
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import subprocess
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import modal
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from modal import App, Image as ModalImage, Volume, Secret
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# =============================================================================
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# CONFIGURATION
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# =============================================================================
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# ── picochat config ────────────────────────────────────────────────────────────
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# depth=8 → model_dim=512, n_heads=4, ~42M params. Trains in ~1-2hr on A10G.
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DEPTH = 8
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MAX_SEQ_LEN = 512
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DEVICE_BATCH_SIZE = 16
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# ── GPU ────────────────────────────────────────────────────────────────────────
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# A10G: cheapest that comfortably fits picochat, no FA3 (SDPA fallback is fine)
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# Switch to "H100:1" (~$3.09/hr) if you want 2x speed
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GPU = "A10G:1"
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# ── Data shards ────────────────────────────────────────────────────────────────
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# picochat needs ~500M tokens → ~8 shards at 250M chars/shard ≈ 4 chars/token
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# Use 12 to have a little extra buffer
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NUM_SHARDS = 12
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# ── W&B ───────────────────────────────────────────────────────────────────────
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# Runs appear in wandb.ai/yoyoliuuu/nanochat
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WANDB_ENTITY = "yoyoliuuu"
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# ── Volume + paths ─────────────────────────────────────────────────────────────
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VOLUME_MOUNT = "/vol"
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NANOCHAT_CACHE = f"{VOLUME_MOUNT}/nanochat_cache"
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# ── Timeouts ──────────────────────────────────────────────────────────────────
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TRAIN_TIMEOUT_SEC = 60 * 60 * 4 # 4h max per picochat run (safe margin)
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DOWNLOAD_TIMEOUT_SEC = 60 * 60 # 1h for data download
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# ── Eval toggle ──────────────────────────────────────────────────────────────
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# CORE metric is expensive (~20-40min). Set to -1 to skip during ablation.
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# val/bpb logged every 250 steps is enough for comparing runs.
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CORE_METRIC_EVERY = -1 # disable mid-training CORE eval to keep runs cheap
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# =============================================================================
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# MODAL PRIMITIVES
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# =============================================================================
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app = App("nanochat-ablation")
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# Persistent volume: data shards, tokenizer, and checkpoints survive restarts
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volume = Volume.from_name("nanochat-vol", create_if_missing=True)
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# Secret: WANDB_API_KEY and HF_TOKEN injected as env vars
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secret = Secret.from_name("nanochat-secrets")
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# Container image — rebuilt automatically if source files change
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image = (
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ModalImage.from_registry("nvidia/cuda:12.8.1-devel-ubuntu24.04", add_python="3.11")
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.apt_install("git", "build-essential", "curl", "wget", "unzip")
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# Copy the nanochat repo (this file's own repo) into the container
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# Modal hashes the directory contents, so changes to gpt.py etc trigger rebuild
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.add_local_dir(
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local_path=".",
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remote_path="/root/nanochat",
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copy=True,
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ignore=[".venv", "__pycache__", "*.pyc", ".git", "rustbpe/target", "runs"],
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)
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.workdir("/root/nanochat")
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# Install uv and project deps (rustbpe installs as a prebuilt wheel from PyPI)
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.run_commands(
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"curl -LsSf https://astral.sh/uv/install.sh | sh",
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"echo 'export PATH=\"$HOME/.local/bin:$PATH\"' >> $HOME/.bashrc",
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)
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.pip_install("uv")
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.run_commands("uv sync --extra gpu --no-install-project")
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# Env vars: nanochat reads NANOCHAT_BASE_DIR to find its cache
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.env({
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"OMP_NUM_THREADS": "1",
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"NANOCHAT_BASE_DIR": NANOCHAT_CACHE,
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"WANDB_ENTITY": WANDB_ENTITY,
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"HF_HOME": f"{VOLUME_MOUNT}/hf_cache",
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})
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)
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# =============================================================================
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# HELPERS
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# =============================================================================
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def _python(module: str, args: list | None = None) -> None:
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args = args or []
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cmd = f"cd /root/nanochat && uv run python -m {module} {' '.join(args)}"
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_run(cmd)
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def _torchrun(module: str, args: list | None = None, *, nproc: int = 1) -> None:
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args = args or []
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args_str = (" -- " + " ".join(args)) if args else ""
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cmd = (
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f"cd /root/nanochat && "
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f"uv run torchrun --standalone --nproc_per_node={nproc} -m {module}{args_str}"
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)
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_run(cmd)
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def _run(cmd: str) -> None:
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print(f"\n>>> {cmd}\n")
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result = subprocess.run(["bash", "-c", cmd], check=False)
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if result.returncode != 0:
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raise RuntimeError(f"Command failed (exit {result.returncode}):\n {cmd}")
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def _setup_cache() -> None:
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"""Create the nanochat cache dir (NANOCHAT_BASE_DIR already points there via env)."""
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os.makedirs(NANOCHAT_CACHE, exist_ok=True)
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# =============================================================================
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# STAGE 0: DATA DOWNLOAD
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# =============================================================================
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@app.function(
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image=image,
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secrets=[secret],
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volumes={VOLUME_MOUNT: volume},
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cpu=8,
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memory=16384,
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timeout=DOWNLOAD_TIMEOUT_SEC,
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)
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def stage_data(num_shards: int = NUM_SHARDS) -> None:
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"""Download FineWeb-EDU shards (CPU-only, cached in volume — runs once for all ablations)."""
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_setup_cache()
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print(f"Downloading {num_shards} FineWeb-EDU shards to volume...")
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_python("nanochat.dataset", [f"-n {num_shards}"])
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volume.commit()
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print("Done.")
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# =============================================================================
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# STAGE 1: TOKENIZER
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# =============================================================================
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@app.function(
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image=image,
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secrets=[secret],
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volumes={VOLUME_MOUNT: volume},
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gpu="A10G:1",
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timeout=60 * 30,
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)
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def stage_tokenizer() -> None:
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"""Train the BPE tokenizer (1 GPU, ~2 min — runs once for all ablations)."""
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_setup_cache()
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tokenizer_path = os.path.join(NANOCHAT_CACHE, "tokenizer.model")
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if os.path.exists(tokenizer_path):
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print("Tokenizer already exists, skipping.")
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else:
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print("Training tokenizer on 2B chars...")
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_python("scripts.tok_train", ["--max-chars=2000000000"])
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volume.commit()
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_python("scripts.tok_eval")
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# =============================================================================
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# STAGE 2: PRETRAIN (parametric — used by all 3 ablations)
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# =============================================================================
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@app.function(
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image=image,
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secrets=[secret],
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volumes={VOLUME_MOUNT: volume},
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gpu=GPU,
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timeout=TRAIN_TIMEOUT_SEC,
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)
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def stage_pretrain(
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run_name: str,
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model_tag: str,
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mlp_type: str = "relu2",
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rope_base: int = 10000,
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) -> None:
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"""
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Pretrain one picochat ablation variant.
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Args:
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run_name: W&B run name (e.g. 'picochat-baseline')
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model_tag: checkpoint directory name (e.g. 'picochat-baseline')
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mlp_type: 'relu2' or 'swiglu'
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rope_base: RoPE base theta (10000 or 500000)
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"""
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_setup_cache()
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print(f"\n{'='*60}")
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print(f"Picochat ablation: {run_name}")
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print(f" mlp_type={mlp_type} rope_base={rope_base}")
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print(f" depth={DEPTH} seq_len={MAX_SEQ_LEN} gpu={GPU}")
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print(f"{'='*60}\n")
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_python("nanochat.report", ["reset"])
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_torchrun(
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"scripts.base_train",
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[
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f"--depth={DEPTH}",
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f"--max-seq-len={MAX_SEQ_LEN}",
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f"--device-batch-size={DEVICE_BATCH_SIZE}",
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f"--window-pattern=L", # full context; SDPA works cleanly with this
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f"--mlp-type={mlp_type}",
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f"--rope-base={rope_base}",
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f"--run={run_name}",
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f"--model-tag={model_tag}",
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f"--core-metric-every={CORE_METRIC_EVERY}", # skip expensive CORE eval
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"--save-every=-1", # only save at end (picochat is fast)
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"--eval-every=100", # val/bpb every 100 steps for dense W&B curves
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],
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)
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volume.commit()
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print(f"Done: {run_name}")
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# =============================================================================
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# INDIVIDUAL ABLATION ENTRYPOINTS
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# (use ::run_baseline etc. to re-run one study after data/tokenizer are ready)
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# =============================================================================
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@app.local_entrypoint()
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def run_baseline() -> None:
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"""Re-run baseline only (requires data+tokenizer already on volume)."""
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stage_pretrain.remote(
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run_name="picochat-baseline",
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model_tag="picochat-baseline",
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mlp_type="relu2",
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rope_base=10000,
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)
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@app.local_entrypoint()
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def run_swiglu() -> None:
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"""Re-run SwiGLU ablation only (requires data+tokenizer already on volume)."""
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stage_pretrain.remote(
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run_name="picochat-swiglu",
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model_tag="picochat-swiglu",
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mlp_type="swiglu",
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rope_base=10000,
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)
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@app.local_entrypoint()
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def run_rope500k() -> None:
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"""Re-run RoPE-500K ablation only (requires data+tokenizer already on volume)."""
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stage_pretrain.remote(
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run_name="picochat-rope500k",
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model_tag="picochat-rope500k",
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mlp_type="relu2",
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rope_base=500000,
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)
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# =============================================================================
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# MAIN ENTRYPOINT: run all 3 ablations
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# =============================================================================
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@app.local_entrypoint()
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def main() -> None:
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"""
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Full ablation pipeline:
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1. Download data (once)
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2. Train tokenizer (once)
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3. Baseline — relu2, RoPE 10K
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4. SwiGLU — swiglu, RoPE 10K
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5. RoPE-500K — relu2, RoPE 500K
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Runs are sequential to stay within budget (~$5-9 total on A10G).
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Data and tokenizer are shared across all 3 training runs via the volume.
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"""
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print("\n" + "="*60)
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print("Picochat Ablation Study | yoyoliuuu/nanochat | W&B")
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print("="*60 + "\n")
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print("[1/5] Downloading data shards...")
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stage_data.remote(num_shards=NUM_SHARDS)
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print("[2/5] Training tokenizer...")
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stage_tokenizer.remote()
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print("[3/5] Training baseline (relu2 + RoPE 10K)...")
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stage_pretrain.remote(
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run_name="picochat-baseline",
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model_tag="picochat-baseline",
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mlp_type="relu2",
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rope_base=10000,
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)
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print("[4/5] Training SwiGLU ablation (swiglu + RoPE 10K)...")
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stage_pretrain.remote(
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run_name="picochat-swiglu",
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model_tag="picochat-swiglu",
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mlp_type="swiglu",
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rope_base=10000,
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)
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print("[5/5] Training RoPE-500K ablation (relu2 + RoPE 500K)...")
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stage_pretrain.remote(
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run_name="picochat-rope500k",
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model_tag="picochat-rope500k",
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mlp_type="relu2",
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rope_base=500000,
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)
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print("\n" + "="*60)
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print("All done! Check W&B at wandb.ai/yoyoliuuu/nanochat")
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print("="*60 + "\n")
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@ -29,6 +29,7 @@ dependencies = [
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[dependency-groups]
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dev = [
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"pytest>=8.0.0",
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"modal>=0.73.0",
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]
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[tool.pytest.ini_options]
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454
uv.lock
454
uv.lock
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@ -2,23 +2,30 @@ version = 1
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revision = 3
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requires-python = ">=3.10"
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resolution-markers = [
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"python_full_version >= '3.12' and sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'",
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"python_full_version >= '3.12' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'",
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"python_full_version >= '3.14' and sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'",
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"python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'",
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"python_full_version >= '3.14' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'",
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"python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'",
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"python_full_version == '3.11.*' and sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'",
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"python_full_version < '3.11' and sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'",
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"python_full_version == '3.11.*' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'",
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"python_full_version < '3.11' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'",
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"python_full_version >= '3.12' and sys_platform == 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
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"python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
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"python_full_version >= '3.14' and sys_platform == 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
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"python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform == 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
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"python_full_version >= '3.14' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
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"python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
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"python_full_version == '3.11.*' and sys_platform == 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
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"python_full_version < '3.11' and sys_platform == 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
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"python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
|
||||
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]
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[[package]]
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||||
name = "wcwidth"
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||||
version = "0.2.14"
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||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user